为实现对车削零件表面粗糙度检测, 提出一种基于机器视觉表面粗糙度检测图像处理的新方法。该方法先按相应算法对所采集图像剔出受光衍射影响严重区域, 然后再按其灰度分布情况进行区域优化, 获得的图像灰度特征参数能反映表面粗糙度量值的有效特征区域。用该方法对表面粗糙度Ra标称值为0.8 μm～12.5 μm的五种车削样件测试, 处理后图像灰度的均值、方差、能量和熵等特征参数与Ra标称值单调关系显著, 各特征曲线的非线性误差均在1.5%以内。对比实验显示, 这种特征提取和区域优化方法可应用于表面粗糙度的区分与检测。

In order to realize the purpose of surface roughness detection for turning parts, a new image processing method for surface roughness detection based on machine vision is proposed. Firstly delete part of collected image that severely affected by diffraction according to corresponding algorithm, and then optimize regions according to gray distribution, so as to obtain image grey feature parameters, which can reflect effective feature areas of surface roughness value. Five turning samples with Ra nominal value ranging from 0.8 μm to 12.5 μm are tested using this method. Feature parameters such as mean value, variance, energy and entropy of processed image have a remarkable monotonic relationship with Ra nominal value. The nonlinear error of each feature parameters relationship curves are all within 1.5%. Contrast experiment results show the method can be applied to distinguish and detect surface roughness.